Algorithms—a finite sequence of steps that transform input into output. In many ways, they’re like mathematical functions. But unlike pure functions, algorithms often have multiple paths to the same end—and some paths are far better than others.

That’s where things get interesting.

As humans, we instinctively seek the best option when presented with choices. Our metric might be comfort, speed, elegance, or even intuition. In computer science, we say we're looking for the most efficient algorithm—typically measured by time and space complexity.

What if we approached learning the same way?

Instead of randomly consuming resources, what if we could design a mental algorithm—an optimized learning strategy—to help us learn faster, retain longer, and understand deeper?

That’s the idea. This isn’t just about learning algorithms. It’s about using algorithmic thinking to master how to learn anything.

Here’s my current hypothesis—my own evolving “learning algorithm”:

• Start with a high level overview: Get a crash course, overview lecture, roadmap, or concept tour—something that gives you the big picture fast. You’re looking for vital context, not depth yet. Think of it as initializing your mental cache. Note you cannot learn it all at once, so do not try to.
• Build something immediately: Don’t wait to “know enough.” Just start. Build proof-of-concepts (POCs), replicate existing projects, and hack things together. Learn by doing. This follows the project-based learning model—because real understanding often comes after getting your hands dirty.
• Study the Masters && Docs: Once you’ve tried, failed, and tinkered, go back to the fundamentals. Read documentation. Reverse-engineer great work. Just like artists study masterpieces, or mathematicians study theorems, you'll absorb nuance better after exposure.

We could stop here—but why not go further? Let’s look at a few arguments that support this hypothesis.

1. The Blank Screen Problem This is one of the most common symptoms of passive learning. You follow tutorials, watch course after course, and yet—when faced with a blank screen—you freeze. No clue where to start. Why? Because consuming information isn’t the same as using it. That paralysis fades when you build. Especially when you don’t know how. The act of hacking your way through a project—Googling errors(issue base learning), connecting concepts, improvising—trains a kind of creative muscle memory. It’s no different from a mathematician grinding through problem sets: over time, confidence compounds. Practice makes intuition.

2. The Creativity Killer: Premature Imitation Then there’s novelty—or rather, the loss of it. When you rely too heavily on someone else’s solution, you risk depriving yourself of the chance to discover your own. Sure, it’s helpful to see how experts do it. But if you skip the struggle, you might miss the most elegant path—your path. Algorithms are rarely one-size-fits-all, and neither are solutions. By clinging to the “approved” way too early, you might be short-circuiting a better idea, a more creative insight, or even a breakthrough. Discovery and innovation don’t come from hand-holding—they come from uncertainty, failure, and relentless tinkering. That’s how science happens. That’s how code evolves. That’s how mastery is born.

Every step in this process is guided by feedback—like a loop. And like any algorithm, it’ll need optimization.

Of course, all advice (including mine) should be taken with a grain of salt. But this isn’t just another learning routine. It’s a theory of learning built by experience, curiosity, and a bit of algorithmic intuition.

Learning isn’t a linear path. It’s a recursive loop—full of dead ends, rewrites, and refactors. But the goal remains the same: to find a mental algorithm that helps you not just consume knowledge, but internalize and apply it with clarity and originality.

This isn’t about rejecting books, lectures, or tutorials. It’s about knowing when to use them—and when to put them aside.

It’s about giving yourself space to fail forward, to tinker intentionally, and to rediscover the joy of figuring things out.

So let’s test this hypothesis—refine it, break it, improve it. Because the best algorithm isn’t just fast or clever. It’s the one that runs well on you.